Results 11 to 20 of about 1,606 (214)
On the convergence of generalized polynomial chaos expansions [PDF]
ESAIM: Mathematical Modelling and Numerical Analysis, 2011 A number of approaches for discretizing partial differential equations with random data are based on generalized polynomial chaos expansions of random variables. These constitute generalizations of the polynomial chaos expansions introduced by Norbert Wiener to expansions in polynomials orthogonal with respect to non-Gaussian probability measures.
Oliver G. Ernst +3 more
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Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification
Journal of Computational Physics, 2023 We present an enriched formulation of the Least Squares (LSQ) regression method for Uncertainty Quantification (UQ) using generalised polynomial chaos (gPC). More specifically, we enrich the linear system with additional equations for the gradient (or sensitivity) of the Quantity of Interest with respect to the stochastic variables. This sensitivity is
Kyriakos Dimitrios Kantarakias +1 more
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A GENERAL FRAMEWORK FOR ENHANCING SPARSITY OF GENERALIZED POLYNOMIAL CHAOS EXPANSIONS [PDF]
International Journal for Uncertainty Quantification, 2019 Compressive sensing has become a powerful addition to uncertainty quantification when only limited data is available. In this paper we provide a general framework to enhance the sparsity of the representation of uncertainty in the form of generalized polynomial chaos expansion.
Yang, Xiu +3 more
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Computation of higher‐order moments of generalized polynomial chaos expansions [PDF]
International Journal for Numerical Methods in Engineering, 2017 SummaryBecause of the complexity of fluid flow solvers, non‐intrusive uncertainty quantification techniques have been developed in aerodynamic simulations in order to compute the quantities of interest required in an optimization process, for example. The objective function is commonly expressed in terms of moments of these quantities, such as the mean,
Savin, É., Faverjon, B.
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Generalized Decoupled Polynomial Chaos for Nonlinear Circuits With Many Random Parameters [PDF]
IEEE Microwave and Wireless Components Letters, 2015 This letter proposes a general and effective decoupled technique for the stochastic simulation of nonlinear circuits via polynomial chaos. According to the standard framework, stochastic circuit waveforms are still expressed as expansions of orthonormal polynomials.
Manfredi, Paolo +3 more
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Generalized Gamma-Laguerre polynomial chaos to model random bending of wearable antennas [PDF]
, 2022 A novel generalized Gamma-Laguerre polynomial chaos expansion is proposed to account for the effect of random variations in lower-bounded design parameters on antenna performance.
Rogier, Hendrik
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An Efficient Polynomial Chaos Expansion Method for Uncertainty Quantification in Dynamic Systems
Applied Mechanics, 2021 Uncertainty is a common feature in first-principles models that are widely used in various engineering problems. Uncertainty quantification (UQ) has become an essential procedure to improve the accuracy and reliability of model predictions.
Jeongeun Son, Yuncheng Du
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Computation, 2022 In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great ...
Georgios Karagiannis +3 more
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Uncertainty analysis for cylindrical structure vibration according to generalized polynomial chaos method [PDF]
, 2023 Aiming to gain an accurate prediction model of the vibro-acoustic problem for the vibrating structure systems, the uncertainty vibration analysis for the cylindrical shell structure considering construction factors is investigated.
Yi, Hong +3 more
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Applied Sciences, 2021 Stability is a well-known challenge for rotating systems supported by hydrodynamic bearings (HDBs), particularly for the condition where the misalignment effect and the parametric uncertainty are considered.
Xiaodong Sun +2 more
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